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Qiang Qiu

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#artificial intelligence Preprint Sep 2026

Beyond ID Embeddings: Process-Grounded Language Modeling for Cognitive Diagnosis

A Process-aware Language Cognitive Diagnosis (PLCD) framework that uses language-derived structures as cognitive priors and response records to calibrate student posterior states is proposed, suggesting that structured language priors calibrated by response records can improve cold-start robustness and cognitive ground...

Ming-Hang Liu, Yuan-Zhuo Wang, Qiang Qiu et al. · 0 citations
#artificial intelligence Preprint Sep 2026

Less Is Personal: Learning Minimal Sufficient User Profiles for Personalized Language Models

This work introduces ENOUGH, a method that iteratively appends behavioral records or emits STOP to construct profiles with adaptive lengths, which consistently outperforms strong heuristic and retrieval-augmented baselines in both effectiveness and efficiency.

Ming-Hang Liu, Qiang Qiu, Yuan-Zhuo Wang et al. · 0 citations
Jul 2026

Steering Diffusion Models via Class-Contrastive Influence for Few-Shot Medical Classification

When labeled data are scarce, off-the-shelf diffusion models can augment training sets for few-shot medical image classification, but not all generated samples are equally useful for the downstream task. Existing approaches largely improve synthetic data by increasing realism, diversity, or domain adaptation, while ove...

Jeeyung Kim, Erfan Esmaeili, Qiang Qiu · 0 citations
Preprint Aug 2026

SEER: A Self-Grounded Evidence Interface for Controlled Spatial Relation Classification

SEER (Self-grounded Evidence for Entity-Relation Reasoning), a training-free inference-time evidence interface for frozen VLMs, is proposed and established as the principal intervention, with reciprocal consistency as a smaller protocol-specific refinement.

Feixiang Liu, Likun Wang, Qiang Qiu et al. · 0 citations
Preprint Jul 2026

Beyond Accuracy: Auditing Spatial Provenance in Visual Token Pruning for OCR-Critical MLLM Inference

This work turns an evidence-risk audit of text-rich multimodal large language models into an evidence-risk audit that couples answer behavior with geometric token-origin provenance, interventions, and realized cost; transparent training-free selectors isolate controlled operating points.

Feixiang Liu, Qiang Qiu, Hao Zhang et al. · 0 citations

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